Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm-Reference-Cited by-同舟云学术

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Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm

Published:2016-03-21 Issue:8 Volume:29 Page:1715-1737
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Rao R. Venkata,Rai Dhiraj P.,Balic J.

Funder

Science and Engineering Research Board (IN)

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/article/10.1007/s10845-016-1210-5/fulltext.html

Reference33 articles.

1. Abhishek, K., Kumar, R. V., Datta, S., & Mahapatra, S. S. (2015). Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching-learning based optimization algorithm). Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1050-8 .

2. Bhavsar, S. N., Aravindan, S., & Rao, P. V. (2015). Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide. Precision Engineering, 40, 131–138.

3. Chandrasekaran, M., Muralidhar, M., Krishna, M. C., & Dixit, U. S. (2010). Application of soft computing techniques in machining performance prediction and optimization: A literature review. International Journal of Advanced Manufacturing Technology, 46, 445–464.

4. Chen, D., Lu, R., Zou, F., & Li, S. (2015). Teaching–learning-based optimization with variable-population scheme and its application for ANN and global optimization. Neurocomputing. doi: 10.1016/j.neucom.2015.08.068 .

5. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. London: Wiley.

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